From the same r/podcasting thread on transcription workflows (reddit.com/r/podcasting/comments/135ljil), a reply that describes half the industry's process: "Show notes are done by feeding portions of my transcript into Chat GPT."
If you've done this, you know the pain. A 60-minute episode produces a 9,000-word transcript. That's over the context window you want to use, or at least over the amount you trust the model to actually read. So you chunk it. Paste 2,000 words, ask for notes, paste the next chunk, ask again, then paste the summaries together and ask for a merge. Forty minutes of clipboard work per episode, and the output still says "in this episode, we delve into" every third paragraph.
This is a solved problem if you structure it right. Here's the honest breakdown.
What good show notes actually contain
Before automating anything, know the target. Show notes that get used:
- A 2-3 sentence summary — what the listener walks away with, not "we had a great conversation."
- Timestamped chapters — "04:30 Why the pricing failed — 17:12 The Singapore launch." The single highest-value block; listeners and apps both use it.
- Key takeaways — 3-6 bullets, each self-contained.
- Guest bio + links — one line, links to their stuff.
- Resources mentioned — list with URLs.
- A subscribe CTA — one line, not three paragraphs.
That's it. Most show notes fail by being either a raw transcript dump or AI foam with no timestamps.
The DIY pipeline: transcript to show notes
This works with any transcript source and any decent LLM. The trick is chunking with overlap and extracting structure, not prose.
Step 1: Get an accurate transcript. Everything downstream inherits its errors. If the guest's name and company are wrong in the transcript, they're wrong in your notes, your title, and your social posts. Whisper locally, or a transcription tool — the important part is fixing names before generation, not after.
Step 2: Chunk with context. Split the transcript into ~10-minute segments at natural topic boundaries. Include the previous segment's last few lines as overlap so the model has continuity.
Step 3: Extract per-chunk. For each chunk, prompt for: topics covered, timestamp ranges, standout quotes (verbatim, with speaker), and any resources mentioned. Extraction prompts beat "summarize this" prompts because they return structure you can verify against the audio.
Step 4: Merge. Paste all chunk outputs into a final prompt: assemble chapters, pick the 3-6 strongest takeaways, draft a 2-sentence summary, list resources. Give it your format template so output is consistent week to week.
Step 5: Verify quotes and timestamps. This is the step everyone skips. LLMs drift timestamps and quietly edit quotes. Spot-check at least the quotes you plan to use in social clips.
Time cost once practiced: 30-45 minutes per episode. The clipboard juggling is real, but it's free and you control the output format exactly.
The recurring costs that push people to tools
Running that pipeline weekly, three things grind:
- Transcript cleanup repeats. Same names, same jargon, same fixes, every episode.
- The pipeline is manual. Chunking, pasting, merging — none of it is hard, all of it is minutes you pay weekly.
- Timestamps drift. If your transcript timestamps aren't word-accurate, every chapter marker you publish is off by a few seconds, and listeners notice.
This is the gap an AI show notes generator is supposed to fill. postwriter.cn's version works from one upload: video or audio in, and you get the transcript, chapter timestamps, description, title options, social copy, and the show notes structure — in one pass.
Two design choices matter more than the feature list. First, the review page aligns text to audio word by word: click any word, hear exactly what was said, correct it. Corrections feed a personal dictionary, so your guests' names and recurring terms stop being errors after the first fix. Second, everything is generated from the same corrected transcript, so your chapters, quotes, and social posts all agree with each other.
It's free during beta, with founder pricing at $39 for 3 years when paid tiers open. For the ChatGPT-chunking crowd, the math is mostly about the 40 minutes per episode of clipboard work, plus $20/month if you were paying for a chat subscription mainly for this.
One template to steal
If you stay DIY, use this merge prompt (adjust to your show):
You are assembling show notes from segment extractions of one podcast episode. Input: per-segment outputs below. Output: (1) 2-sentence summary, (2) chapter list as MM:SS — title, merging duplicate topics, (3) 3-6 key takeaways as bullets, each ≤20 words, (4) up to 3 verbatim quotes with speaker attribution, (5) resources list. Do not invent anything not present in the input. Use plain language, no filler phrases.
The last two sentences do most of the work.
FAQ
How long should show notes be?
150-400 words plus timestamps and links. Long-form show notes (800+) work for some niches, but skim data shows most readers use the chapters and stop.
Do timestamps help SEO?
They give apps and Google structured entry points to your episode, and they appear in search snippets for podcast results. Cheap to include, non-zero return.
Can I generate show notes from YouTube auto-captions?
You can, but you inherit their errors — names and niche terms especially. Clean the transcript first or the mistakes propagate everywhere.
What does postwriter.cn cost after beta?
Founder pricing is $39 for a 3-year term. Comparable tooling in this space runs $12-29/month.
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